Río Bueno , Manuel delDelicado, Pedro2023-06-202023-06-202003-021048-525210.1080/1048525031000074523https://hdl.handle.net/20.500.14352/50269We introduce nonparametric density estimators that generalize the classical histogram and frequency polygon. The new estimators are expressed as linear combinations of density functions that are piecewise polynomials, where the coefficients are optimally chosen in order to minimize an approximate version of the integrated square error of the estimator. We establish the asymptotic behaviour of the proposed estimators, and study their performance in a simulation study.journal articlehttp://www.econ.upf.edu/ca/recerca/paper.php?id=422metadata only access519.8ConvolutionFrequency polygonNonparametric density estimationSimulationSplinesToeplitz matrixKerrnel density estimatorsBinned dataInvestigación operativa (Matemáticas)1207 Investigación Operativa